OpenRaiser/Intern-Atlas huggingface dataset:
MAPo : Motion-Aware Partitioning of Deformable 3D Gaussian Splatting for High-Fidelity Dynamic Scene Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

3D Gaussian Splatting, known for enabling high-quality static scene reconstruction with fast rendering, is increasingly being applied to multi-view dynamic scene reconstruction. A common strategy involves learning a deformation field to model the temporal changes of a canonical set of 3D Gaussians. However, these deformation-based methods often produce blurred renderings and lose fine motion details in highly dynamic regions due to the inherent limitations of a single, unified model in representing diverse motion patterns. To address these challenges, we introduce Motion-Aware Partitioning of Deformable 3D Gaussian Splatting (MAPo), a novel framework for high-fidelity dynamic scene reconstruction. Its core is a dynamic score-based partitioning strategy that distinguishes between high- and low-dynamic 3D Gaussians. For high-dynamic 3D Gaussians, we recursively partition them temporally and duplicate their deformation networks for each new temporal segment, enabling specialized modeling to capture intricate motion details. Concurrently, low-dynamic 3DGs are treated as static to reduce computational costs. However, this temporal partitioning strategy for high-dynamic 3DGs can introduce visual discontinuities across frames at the partition boundaries. To address this, we introduce a cross-frame consistency loss, which not only ensures visual continuity but also further enhances rendering quality. Extensive experiments demonstrate that MAPo achieves superior rendering quality compared to baselines while maintaining comparable computational costs, particularly in regions with complex or rapid motions.
Context-Aware Risk Estimation in Home Environments: A Probabilistic Framework for Service Robots
Venue: arXiv cs.RO 2025
Year: 2025

We present a novel framework for estimating accident-prone regions in everyday indoor scenes, aimed at improving real-time risk awareness in service robots operating in human-centric environments. As robots become integrated into daily life, particularly in homes, the ability to anticipate and respond to environmental hazards is crucial for ensuring user safety, trust, and effective human-robot interaction. Our approach models object-level risk and context through a semantic graph-based propagation algorithm. Each object is represented as a node with an associated risk score, and risk propagates asymmetrically from high-risk to low-risk objects based on spatial proximity and accident relationship. This enables the robot to infer potential hazards even when they are not explicitly visible or labeled. Designed for interpretability and lightweight onboard deployment, our method is validated on a dataset with human-annotated risk regions, achieving a binary risk detection accuracy of 75%. The system demonstrates strong alignment with human perception, particularly in scenes involving sharp or unstable objects. These results underline the potential of context-aware risk reasoning to enhance robotic scene understanding and proactive safety behaviors in shared human-robot spaces. This framework could serve as a foundation for future systems that make context-driven safety decisions, provide real-time alerts, or autonomously assist users in avoiding or mitigating hazards within home environments.
Bootstrapping Learned Cost Models with Synthetic SQL Queries
Venue: arXiv cs.DB 2025
Year: 2025

Having access to realistic workloads for a given database instance is extremely important to enable stress and vulnerability testing, as well as to optimize for cost and performance. Recent advances in learned cost models have shown that when enough diverse SQL queries are available, one can effectively and efficiently predict the cost of running a given query against a specific database engine. In this paper, we describe our experience in exploiting modern synthetic data generation techniques, inspired by the generative AI and LLM community, to create high-quality datasets enabling the effective training of such learned cost models. Initial results show that we can improve a learned cost model's predictive accuracy by training it with 45% fewer queries than when using competitive generation approaches.
A Standing Support Mobility Robot for Enhancing Independence in Elderly Daily Living
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a standing support mobility robot "Moby" developed to enhance independence and safety for elderly individuals during daily activities such as toilet transfers. Unlike conventional seated mobility aids, the robot maintains users in an upright posture, reducing physical strain, supporting natural social interaction at eye level, and fostering a greater sense of self-efficacy. Moby offers a novel alternative by functioning both passively and with mobility support, enabling users to perform daily tasks more independently. Its main advantages include ease of use, lightweight design, comfort, versatility, and effective sit-to-stand assistance. The robot leverages the Robot Operating System (ROS) for seamless control, featuring manual and autonomous operation modes. A custom control system enables safe and intuitive interaction, while the integration with NAV2 and LiDAR allows for robust navigation capabilities. This paper reviews existing mobility solutions and compares them to Moby, details the robot's design, and presents objective and subjective experimental results using the NASA-TLX method and time comparisons to other methods to validate our design criteria and demonstrate the advantages of our contribution.
Design and Analysis of the Tail Sequence for Short LDPC-Coded Space Communications
Venue: arXiv cs.IT 2025
Year: 2025

According to some standards for satellite communications, the transmitted stream is divided into transmission units with variable length, for which detecting the termination is particularly relevant. This is the case of space TeleCommands (TCs), where coded data are usually preceded by a start sequence, and optionally followed by a tail sequence, forming the Communication Link Transmission Unit (CLTU). Regarding the choice of schemes for error correction, the Consultative Committee for Space Data Systems recommendations for TC synchronization and coding suggests to use, among others, two Low-Density Parity-Check (LDPC) codes: one (relatively) long and one short. Adopting the long LDPC code eliminates the need for a tail sequence, as the LDPC decoder always fails when overrunning the end of the CLTU, thus causing the decoding and detection process to stop. This, however, is not true when the short LDPC code is adopted, since its decoding might converge on a codeword even when the decoder input is not a noisy codeword. This makes it necessary to use a tail sequence that causes the decoder to fail regardless of its input. In this paper, we study the features required for such a sequence and propose some methods for its design. Our numerical results, obtained considering various detection approaches for the tail sequence, show that the overall TC rejection probability improves significantly when the proposed tail sequence is employed. Our simulations also show that, for moderate values of the Signal-to-Noise Ratio (SNR), with a properly designed tail sequence it is possible to obtain the same performance in terms of TC rejection probability using decoder-based detection and likelihood ratio test-based detection, with the former approach being less complex than the latter.
TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations
Venue: arXiv cs.CV 2025
Year: 2025

With the introduction of vehicles with autonomous capabilities on public roads, predicting pedestrian crossing intention has emerged as an active area of research. The task of predicting pedestrian crossing intention involves determining whether pedestrians in the scene are likely to cross the road or not. In this work, we propose TrajFusionNet, a novel transformer-based model that combines future pedestrian trajectory and vehicle speed predictions as priors for predicting crossing intention. TrajFusionNet comprises two branches: a Sequence Attention Module (SAM) and a Visual Attention Module (VAM). The SAM branch learns from a sequential representation of the observed and predicted pedestrian trajectory and vehicle speed. Complementarily, the VAM branch enables learning from a visual representation of the predicted pedestrian trajectory by overlaying predicted pedestrian bounding boxes onto scene images. By utilizing a small number of lightweight modalities, TrajFusionNet achieves the lowest total inference time (including model runtime and data preprocessing) among current state-of-the-art approaches. In terms of performance, it achieves state-of-the-art results across the three most commonly used datasets for pedestrian crossing intention prediction.
The IRMA Dataset: A Structured Audio-MIDI Corpus for Iranian Classical Music
Venue: arXiv cs.SD 2025
Year: 2025

We present the IRMA Dataset (Iranian Radif MIDI Audio), a multi-level, open-access corpus designed for the computational study of Iranian classical music, with a particular emphasis on the radif, a structured repertoire of modal-melodic units central to pedagogy and performance. The dataset combines symbolic MIDI representations, phrase-level audio-MIDI alignment, musicological transcriptions in PDF format, and comparative tables of theoretical information curated from a range of performers and scholars. We outline the multi-phase construction process, including segment annotation, alignment methods, and a structured system of identifier codes to reference individual musical units. The current release includes the complete radif of Karimi; MIDI files and metadata from Mirza Abdollah's radif; selected segments from the vocal radif of Davami, as transcribed by Payvar and Fereyduni; and a dedicated section featuring audio-MIDI examples of tahrir ornamentation performed by prominent 20th-century vocalists. While the symbolic and analytical components are released under an open-access license (CC BY-NC 4.0), some referenced audio recordings and third-party transcriptions are cited using discographic information to enable users to locate the original materials independently, pending copyright permission. Serving both as a scholarly archive and a resource for computational analysis, this dataset supports applications in ethnomusicology, pedagogy, symbolic audio research, cultural heritage preservation, and AI-driven tasks such as automatic transcription and music generation. We welcome collaboration and feedback to support its ongoing refinement and broader integration into musicological and machine learning workflows.
AI-Powered Detection of Inappropriate Language in Medical School Curricula
Venue: arXiv cs.CL 2025
Year: 2025

The use of inappropriate language -- such as outdated, exclusionary, or non-patient-centered terms -- medical instructional materials can significantly influence clinical training, patient interactions, and health outcomes. Despite their reputability, many materials developed over past decades contain examples now considered inappropriate by current medical standards. Given the volume of curricular content, manually identifying instances of inappropriate use of language (IUL) and its subcategories for systematic review is prohibitively costly and impractical. To address this challenge, we conduct a first-in-class evaluation of small language models (SLMs) fine-tuned on labeled data and pre-trained LLMs with in-context learning on a dataset containing approximately 500 documents and over 12,000 pages. For SLMs, we consider: (1) a general IUL classifier, (2) subcategory-specific binary classifiers, (3) a multilabel classifier, and (4) a two-stage hierarchical pipeline for general IUL detection followed by multilabel classification. For LLMs, we consider variations of prompts that include subcategory definitions and/or shots. We found that both LLama-3 8B and 70B, even with carefully curated shots, are largely outperformed by SLMs. While the multilabel classifier performs best on annotated data, supplementing training with unflagged excerpts as negative examples boosts the specific classifiers' AUC by up to 25%, making them most effective models for mitigating harmful language in medical curricula.
Bangla-Bayanno: A 52K-Pair Bengali Visual Question Answering Dataset with LLM-Assisted Translation Refinement
Venue: arXiv cs.CL 2025
Year: 2025

In this paper, we introduce Bangla-Bayanno, an open-ended Visual Question Answering (VQA) Dataset in Bangla, a widely used, low-resource language in multimodal AI research. The majority of existing datasets are either manually annotated with an emphasis on a specific domain, query type, or answer type or are constrained by niche answer formats. In order to mitigate human-induced errors and guarantee lucidity, we implemented a multilingual LLM-assisted translation refinement pipeline. This dataset overcomes the issues of low-quality translations from multilingual sources. The dataset comprises 52,650 question-answer pairs across 4750+ images. Questions are classified into three distinct answer types: nominal (short descriptive), quantitative (numeric), and polar (yes/no). Bangla-Bayanno provides the most comprehensive open-source, high-quality VQA benchmark in Bangla, aiming to advance research in low-resource multimodal learning and facilitate the development of more inclusive AI systems.
Distributed Sparsest Cut via Eigenvalue Estimation
Venue: arXiv cs.DS 2025
Year: 2025

We give new, improved bounds for approximating the sparsest cut value or in other words the conductance $ϕ$ of a graph in the CONGEST model. As our main result, we present an algorithm running in $O(\log^2 n/ϕ)$ rounds in which every vertex outputs a value $\tilde ϕ$ satisfying $ϕ\le \tilde ϕ\le \sqrt{2.01ϕ}$. In most regimes, our algorithm improves significantly over the previously fastest algorithm for the problem [Chen, Meierhans, Probst Gutenberg, Saranurak; SODA 25]. Additionally, our result generalizes to $k$-way conductance. We obtain these results, by approximating the eigenvalues of the normalized Laplacian matrix $L:=I-\rm{Deg}^{-1/2}A\rm{Deg}^ {-1/2}$, where, $A$ is the adjacency matrix and $\rm{Deg}$ is the diagonal matrix with the weighted degrees on the diagonal. The previous state of the art sparsest cut algorithm is in the technical realm of expander decompositions. Our algorithms, on the other hand, are relatively simple and easy to implement. At the core, they rely on the well-known power method, which comes down to repeatedly multiplying the Laplacian with a vector. This operation can be performed in a single round in the CONGEST model. All our algorithms apply to weighted, undirected graphs. Our lower bounds apply even in unweighted graphs.
GegenNet: Spectral Convolutional Neural Networks for Link Sign Prediction in Signed Bipartite Graphs
Venue: arXiv cs.LG 2025
Year: 2025

Given a signed bipartite graph (SBG) G with two disjoint node sets U and V, the goal of link sign prediction is to predict the signs of potential links connecting U and V based on known positive and negative edges in G. The majority of existing solutions towards link sign prediction mainly focus on unipartite signed graphs, which are sub-optimal due to the neglect of node heterogeneity and unique bipartite characteristics of SBGs. To this end, recent studies adapt graph neural networks to SBGs by introducing message-passing schemes for both inter-partition (UxV) and intra-partition (UxU or VxV) node pairs. However, the fundamental spectral convolutional operators were originally designed for positive links in unsigned graphs, and thus, are not optimal for inferring missing positive or negative links from known ones in SBGs. Motivated by this, this paper proposes GegenNet, a novel and effective spectral convolutional neural network model for link sign prediction in SBGs. In particular, GegenNet achieves enhanced model capacity and high predictive accuracy through three main technical contributions: (i) fast and theoretically grounded spectral decomposition techniques for node feature initialization; (ii) a new spectral graph filter based on the Gegenbauer polynomial basis; and (iii) multi-layer sign-aware spectral convolutional networks alternating Gegenbauer polynomial filters with positive and negative edges. Our extensive empirical studies reveal that GegenNet can achieve significantly superior performance (up to a gain of 4.28% in AUC and 11.69% in F1) in link sign prediction compared to 11 strong competitors over 6 benchmark SBG datasets.
Renyi partial orders for BISO channels
Venue: arXiv cs.IT 2025
Year: 2025

A fundamental question in information theory is to quantify the loss of information under a noisy channel. Partial orders are typical tools to that end, however, they are often also challenging to evaluate. For the special class of binary input symmetric output (BISO) channels, Geng et al. showed that among channels with the same capacity, the binary symmetric channel (BSC) and binary erasure channel (BEC) are extremal with respect to the more capable order. Here we extend on this result by considering partial orders based on Renyi mutual information. We establish the extremality of the BSC and BEC in this setting with respect to the generalized Renyi capacity. In the process, we also generalize the needed tools and introduce $α$-Lorenz curves.
CapTune: Adapting Non-Speech Captions With Anchored Generative Models
Venue: arXiv cs.HC 2025
Year: 2025

Non-speech captions are essential to the video experience of deaf and hard of hearing (DHH) viewers, yet conventional approaches often overlook the diversity of their preferences. We present CapTune, a system that enables customization of non-speech captions based on DHH viewers' needs while preserving creator intent. CapTune allows caption authors to define safe transformation spaces using concrete examples and empowers viewers to personalize captions across four dimensions: level of detail, expressiveness, sound representation method, and genre alignment. Evaluations with seven caption creators and twelve DHH participants showed that CapTune supported creators' creative control while enhancing viewers' emotional engagement with content. Our findings also reveal trade-offs between information richness and cognitive load, tensions between interpretive and descriptive representations of sound, and the context-dependent nature of caption preferences.
Short-Horizon Predictive Maintenance of Industrial Pumps Using Time-Series Features and Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

This study presents a machine learning framework for forecasting short-term faults in industrial centrifugal pumps using real-time sensor data. The approach aims to predict {EarlyWarning} conditions 5, 15, and 30 minutes in advance based on patterns extracted from historical operation. Two lookback periods, 60 minutes and 120 minutes, were evaluated using a sliding window approach. For each window, statistical features including mean, standard deviation, minimum, maximum, and linear trend were extracted, and class imbalance was addressed using the SMOTE algorithm. Random Forest and XGBoost classifiers were trained and tested on the labeled dataset. Results show that the Random Forest model achieved the best short-term forecasting performance with a 60-minute window, reaching recall scores of 69.2\% at 5 minutes, 64.9\% at 15 minutes, and 48.6\% at 30 minutes. With a 120-minute window, the Random Forest model achieved 57.6\% recall at 5 minutes, and improved predictive accuracy of 65.6\% at both 15 and 30 minutes. XGBoost displayed similar but slightly lower performance. These findings highlight that optimal history length depends on the prediction horizon, and that different fault patterns may evolve at different timescales. The proposed method offers an interpretable and scalable solution for integrating predictive maintenance into real-time industrial monitoring systems.
Reducing Street Parking Search Time via Smart Assignment Strategies
Venue: arXiv cs.LG 2025
Year: 2025

In dense metropolitan areas, searching for street parking adds to traffic congestion. Like many other problems, real-time assistants based on mobile phones have been proposed, but their effectiveness is understudied. This work quantifies how varying levels of user coordination and information availability through such apps impact search time and the probability of finding street parking. Through a data-driven simulation of Madrid's street parking ecosystem, we analyze four distinct strategies: uncoordinated search (Unc-Agn), coordinated parking without awareness of non-users (Cord-Agn), an idealized oracle system that knows the positions of all non-users (Cord-Oracle), and our novel/practical Cord-Approx strategy that estimates non-users' behavior probabilistically. The Cord-Approx strategy, instead of requiring knowledge of how close non-users are to a certain spot in order to decide whether to navigate toward it, uses past occupancy distributions to elongate physical distances between system users and alternative parking spots, and then solves a Hungarian matching problem to dispatch accordingly. In high-fidelity simulations of Madrid's parking network with real traffic data, users of Cord-Approx averaged 6.69 minutes to find parking, compared to 19.98 minutes for non-users without an app. A zone-level snapshot shows that Cord-Approx reduces search time for system users by 72% (range = 67-76%) in central hubs, and up to 73% in residential areas, relative to non-users.
Heterogeneous Self-Supervised Acoustic Pre-Training with Local Constraints
Venue: arXiv cs.LG 2025
Year: 2025

Self-supervised pre-training using unlabeled data is widely used in automatic speech recognition. In this paper, we propose a new self-supervised pre-training approach to dealing with heterogeneous data. Instead of mixing all the data and minimizing the averaged global loss in the conventional way, we impose additional local constraints to ensure that the model optimizes each source of heterogeneous data to its local optimum after $K$-step gradient descent initialized from the model. We formulate this as a bilevel optimization problem, and use the first-order approximation method to solve the problem. We discuss its connection to model-agnostic meta learning. Experiments are carried out on self-supervised pre-training using multi-domain and multilingual datasets, demonstrating that the proposed approach can significantly improve the adaptivity of the self-supervised pre-trained model for the downstream supervised fine-tuning tasks.
Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence
Venue: arXiv cs.LG 2025
Year: 2025

Most existing Large Language Model (LLM)-based agent frameworks rely on centralized orchestration, incurring high deployment costs, rigid communication topologies, and limited adaptability. To address these challenges, we introduce Symphony, a decentralized multi-agent system which enables lightweight LLMs on consumer-grade GPUs to coordinate. Symphony introduces three key mechanisms: (1) a decentralized ledger that records capabilities, (2) a Beacon-selection protocol for dynamic task allocation, and (3) weighted result voting based on CoTs. This design forms a privacy-saving, scalable, and fault-tolerant orchestration with low overhead. Empirically, Symphony outperforms existing baselines on reasoning benchmarks, achieving substantial accuracy gains and demonstrating robustness across models of varying capacities.